Nvidia Ontology Setup
NVIDIAOfficialCreated by NVIDIA
- Skill
- Workflow
- Cloud and DevOps
- Apache-2.0
- Medium risk
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured approach to deploying and maintaining the Auto Ontology runtime. It covers official Helm chart installations for Kubernetes, local Docker Compose setups, and developer-specific workflows. It also includes guidance on connecting MCP clients to existing deployments and troubleshooting common runtime issues, such as authentication failures, database connection problems, and ingestion errors.
What it does
- Guides users through official Helm chart deployment for Kubernetes.
- Provides instructions for running the full stack locally via Docker Compose.
- Supports developer-specific workflows for modifying the Auto Ontology codebase.
- Facilitates MCP client connection to existing Auto Ontology instances.
- Offers troubleshooting steps for common runtime, authentication, and ingestion issues.
- Explains how to manage database connections via the UI or environment variables.
When to use it
- When you need to perform a fresh installation of the Auto Ontology runtime.
- When you need to troubleshoot connectivity, authentication, or ingestion failures.
- When you are a developer looking to set up a local environment to modify the platform.
- When you need to connect an MCP client to an already running Auto Ontology deployment.
Advantages
- Centralizes installation and troubleshooting procedures in one place.
- Provides clear distinctions between production (Helm) and local (Compose) deployment paths.
- Includes specific guidance for MCP integration and OAuth discovery.
- Reduces reliance on external documentation by providing built-in troubleshooting steps.
Usage examples
- “How do I install Auto Ontology on Kubernetes?”
- The agent will guide you through using the official Helm chart, including setting up the required values file for secrets and configuration.
- “My Auto Ontology ingestion is failing, what should I check?”
- The agent will direct you to verify your NVIDIA NIM key, check database connection strings, and review the ingestion service logs.
- “How do I connect an MCP client to my existing Auto Ontology instance?”
- The agent will explain how to point the MCP client to your web app URL and verify OAuth discovery before connecting.
- “I am a developer, how do I set up the environment to work on the code?”
- The agent will provide instructions for using the developer setup scripts and configuring the local environment for frontend and backend development.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/nvidia-ontology-setup/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nvidia-ontology-setup.zip -d .agents/skills
Documentation by NVIDIA
<!-- SPDX-FileCopyrightText: Copyright (c) 2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved. SPDX-License-Identifier: Apache-2.0 -->
Auto Ontology setup
Purpose
Bring up the current Auto Ontology implementation, troubleshoot it, or connect an agent
to an instance that is already running. Do not invent a second installer:
the official installation is the Helm chart in repository-root DEPLOYMENT.md;
Docker Compose runs the same stack on one machine. dev_tools/setup_env.sh is a
developer tool, not an installer. Typed setup, connection, ingestion,
compilation, and readiness artifacts are in
runtime-contract.yaml.
On failure, read troubleshooting.md instead of searching the web.
Instructions
Required questions
Ask these if not already clear. Do not guess a default and emit a command.
Target — Helm on Kubernetes (the official install, and the default recommendation), Docker Compose on one machine, a developer workflow (
--dev/--ds, only when changing Auto Ontology itself), or "Auto Ontology is already up, I only need MCP"?NVIDIA NIM key — is
DEFAULT_MODELS_API_KEY(Helm:defaultModelsApiKey) available? Chat and ingest need it.Admin account — the email and password for the bootstrap admin. Self-service sign-up is disabled, so this is the only way to sign in.
Source database — a connection string (
CONNECTION_STRINGS/connectionStrings), or will connections be added in the UI?Helm only — which chart version, and the URL users will browse to (
appUrl).Compose only — is host
5432free?POSTGRES_PORTonly remaps the host side; containers still use 5432 internally.
Never ask the user to paste secrets into the conversation, and never put them
in command arguments. Have the user write them into a values file or .env.
Install with Helm (official)
Releases are published to the public NGC nvidia org (chart and images). Follow
DEPLOYMENT.md with the version the user named:
The user creates a values file in an editor (not on the command line, so values stay out of shell history and process arguments), readable only by them, and out of git:
# auto-ontology-values.yaml
defaultModelsApiKey: <API-KEY>
postgresPassword: <POSTGRES-PASSWORD>
adminEmail: <ADMIN-EMAIL>
adminPassword: <ADMIN-PASSWORD>
connectionStrings: <CONNECTION-STRINGS> # optional; or add connections in the UIhelm fetch https://helm.ngc.nvidia.com/nvidia/charts/auto-ontology-<VERSION>.tgz
helm install auto-ontology auto-ontology-<VERSION>.tgz -f auto-ontology-values.yaml
kubectl port-forward svc/frontend 3000:3000adminEmailandadminPasswordare required; the install fails without them.appUrlmust be the exact origin users browse to. The default (http://localhost:3000) only fits the port-forward above; set it for a NodePort, ingress, or HTTPS origin.authSecretis generated on first install and kept across upgrades; set it only to share one value across environments.If the chart fetch returns 404, that version is not published yet. Ask the user; do not fall back to the internal staging registry.
This skill does not cover Astra GitOps.
Local: Docker Compose
For running the whole stack on one machine. In the repository root, the user
copies .env.example to .env and fills in AUTH_SECRET, APP_URL,
AUTO_ONTOLOGY_ADMIN_EMAIL, and AUTO_ONTOLOGY_ADMIN_PASSWORD (all required).
Then:
docker compose up -d --buildAUTH_SECRET and APP_URL are required. Without them every page fails with a
Better Auth "default secret" error. Fill DEFAULT_MODELS_API_KEY and a
CONNECTION_STRINGS value (or plan to add connections in the UI). The full
variable list is .env.example.
This builds auto-ontology and auto-ontology-frontend, starts Postgres,
pgAdmin, and the ingestion service, runs two one-shot migrate jobs, and starts
the app:
auto-ontology-migrateapplies Alembic migrations to the backend'spublicschema (catalog and semantic tier). The backend waits for it.frontend-migratesyncs the Prismafrontendschema: users, sessions, API keys, and the OAuth tables MCP sign-in uses. The frontend waits for it.
If sign-in or MCP login fails, check both with docker compose ps -a and
docker compose logs frontend-migrate. Read the frontend-migrate log even
when it exited 0: it has finished without an error before while leaving MCP
login broken.
| Service | URL |
|---|---|
| UI | http://localhost:3000 |
| FastAPI (internal) | http://localhost:3001 |
| Ingestion | http://localhost:3002 |
| pgAdmin | http://localhost:5050 |
| Postgres | localhost:$POSTGRES_PORT (from .env, default 5432) |
Developer workflows (--dev, --ds)
Only for people changing Auto Ontology itself; these are not installations.
dev_tools/setup_env.sh starts part of the stack in Docker and leaves the rest
to run from the checkout. Both need the .env above plus:
cd frontend && pnpm install # Next.js
# from repo root:
uv sync--dev runs infra only (Postgres, pgAdmin, ingestion); you run Next.js and FastAPI:
./dev_tools/setup_env.sh --dev
cd frontend && pnpm dev
# repo root:
uv run uvicorn auto_ontology.server.__main__:create_app --factory --reload --host 127.0.0.1 --port 3001The app factory is create_app() in auto_ontology/server/__main__.py. There is no
auto_ontology/server/main.py. uv run python -m auto_ontology.server is the same app without
reload (what the container runs).
--ds runs the frontend in Docker and FastAPI on the host:
./dev_tools/setup_env.sh --ds
uv run python -m auto_ontology.serverThe frontend image bakes PYTHON_API_URL=http://host.docker.internal:3001, so
the API must accept connections from the container, not just loopback.
python -m auto_ontology.server binds 0.0.0.0:3001 (what setup_env.sh --ds
prints); a --host 127.0.0.1 uvicorn is unreachable from the container on
Linux. Because FastAPI trusts x-auto-ontology-user-id, keep port 3001
firewalled from other machines. On native Linux Docker you may need
--add-host=host.docker.internal:host-gateway.
Limitations
This skill uses the repository's existing Helm chart and Compose file and does not cover Astra GitOps, change semantic definitions, or treat partial Vault configuration as secret storage. Ask before destructive volume deletion.
Auto Ontology is already up — MCP only
Do not reinstall. Point AUTO_ONTOLOGY_API_URL at the web app (Compose UI is
:3000, not FastAPI :3001):
AUTO_ONTOLOGY_API_URL=http://localhost:3000 uvx --from "git+https://github.com/NVIDIA/auto-ontology.git#subdirectory=mcp" auto-ontology-mcpUntil the package is on PyPI this needs GitHub credentials that can read
NVIDIA/auto-ontology. Client config uses the MCP server URL with a /mcp suffix.
People sign in through Auto Ontology; do not put a token on the MCP server.
Confirm the deployment is new enough to be an authorization server:
curl -s -o /dev/null -w '%{http_code}\n' "$AUTO_ONTOLOGY_API_URL/.well-known/oauth-authorization-server"200 is required. Anything else: run the frontend from the checkout
(pnpm dev) and point AUTO_ONTOLOGY_API_URL at that port.
Examples
Kubernetes: install the published Helm chart from
DEPLOYMENT.mdwith the admin account andappUrlset.One machine:
docker compose up -d --buildafter filling.env.Existing deployment: do not reinstall; configure
AUTO_ONTOLOGY_API_URLand verify OAuth discovery before connecting the MCP client.
Connect a source
Connections come from one of two places, and only one is used:
UI-managed (Settings → Connections, or the API below). When any exist,
CONNECTION_STRINGSis ignored.CONNECTION_STRINGSin.envis a fallback used only when there are no UI-managed connections.GET /api/connections/sourcereports whether it is set; it cannot carry schema or table filters.
To add a UI-managed connection (permission connection:manage):
POST /api/connections/testvalidates credentials and returns the schemas for the allowlist. A 422 carries the driver's error; fix it before creating.POST /api/connectionsstores the connection. The 201 only means it was saved: ingestion is triggered best-effort and may fail or still be running.Verify it landed:
GET /api/datasources/dbs(catalog:read) lists the database with the expected tables,GET /api/semantic-compilation/statusreports progress, and MCPcheck_readinessreports no blockers.
Scope is set per connection: a schema allowlist, plus table_allow_regex /
table_deny_regex applied at catalog extraction (deny wins; unqualified,
case-sensitive names). Env connections have neither. The full table-level scope
guarantee is still open in
#255, so if particular tables must never be
catalogued, sampled, or embedded, say that this is not yet enforceable
end-to-end rather than implying it is.
Verify
UI loads at http://localhost:3000 (or the deployed
APP_URL/ HelmappUrl) and shows the left navigation. Missing nav → stale cookie; see troubleshooting.md.GET /api/semantic-compilation/statuson the web origin authenticates (cookie,x-api-key, or SSO bearer) and returns JSON.calculated: falsemeans the glossary is empty — ingest / compile, do not keep retrying chat.MCP: OAuth discovery returns 200 (above). Then
check_readinessonce a client is connected.
Stop
helm uninstall auto-ontology # Helm; also deletes the postgres-data PVC and its data
docker compose down # Compose; keep volumes
docker compose down -v # Compose; wipe Postgres / pgAdmin dataBoth helm uninstall and docker compose down -v destroy data; ask first.
Troubleshooting
Use troubleshooting.md for stale sessions, model-key failures, embedding mismatches, port conflicts, and partial Vault configuration. Redact secrets before sharing command output as evidence.
See also
nvidia-ontology-query— call Auto Ontology once it is runningnvidia-ontology-management— edit the semantic layerCLAUDE.md— maintain Auto Ontology itself
Files
- ATTRIBUTION.mdAdded by AutoGineer921 B
- BENCHMARK.md9.0 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md10.8 KiB
- assets/runtime-contract.yaml2.2 KiB
- evals/evals.json9.1 KiB
- references/troubleshooting.md4.5 KiB
- skill-card.md4.8 KiB
- skill.oms.sig5.0 KiB
LICENSE
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Needs: Needs an MCP server, Needs network access, Needs an API key, Needs a command-line tool, Needs Python, Needs Node.js
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Attribution and licence
- Created by
- NVIDIA
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/nvidia-ontology-setup · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by NVIDIA or NVIDIA.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nvidia-ontology-setup.zip